app.py

import streamlit as st
import os 
from transformers import pipeline
from bardapi import Bard 
import os 
from getvalues import getValues
from pymongo import MongoClient
from streamlit_option_menu import option_menu
import pandas as pd 

uri = os.environ.get("MONGO_CONNECTION_STRING")
conn = MongoClient(uri, tlsCertificateKeyFile="database/cert.pem")

db = conn["myapp"]
col = db["reminders"]

bardkey = os.environ.get("BARD_API_KEY")

bard = Bard(token=bardkey)


classifi = pipeline(model="facebook/bart-large-mnli")

def view_rem():
    allrem = list(col.find())
    remdata = pd.DataFrame(allrem)
    st.dataframe(remdata)

def chatbot():
    st.title("ChatBot")
    if query := st.chat_input("Enter your message"):
        ans = classifi(query, candidate_labels=["Reminder", "General Conversation"])
        if ans["labels"][0] == "Reminder":
            values = getValues(query.lower())
            with st.chat_message("assistant"):
                st.write(values)
                col.insert_one(values)
        elif ans["labels"][0] == "General Conversation":
            umsg = bard.get_answer(message)["content"]
            with st.chat_message("assistant"):
                st.write(umsg)
            
            
with st.sidebar:
    selected = option_menu(None, options=["Chatbot", "View Reminders"])
    if selected == "Chatbot":
        chatbot()
    elif selected == "View Reminders":
        view_rem()